{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Echogram Plotting Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script demonstrates plotting echograms using fake data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import datetime\n",
    "from matplotlib.pyplot import figure, show\n",
    "from echolab2.processing import processed_data, line\n",
    "from echolab2.plotting.matplotlib import echogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pings = 100\n",
    "test_data_samples = 1000\n",
    "sample_thickness_m = 0.5\n",
    "ping_interval_ms = 1000.0\n",
    "\n",
    "fake_Sv = processed_data.ProcessedData('Fake Data', 120000, 'Sv')\n",
    "fake_Sv.n_samples = test_data_samples\n",
    "fake_Sv.n_pings = test_data_pings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create data arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create some fake data arrays.\n",
    "ranges = np.arange(test_data_samples) * sample_thickness_m\n",
    "times = (np.arange(test_data_pings) * ping_interval_ms) + \\\n",
    "        np.datetime64('2018-03-21T03:30:30', 'ms').astype('float')\n",
    "data = np.zeros((test_data_pings, test_data_samples), dtype='float32')\n",
    "data[:, 1::2] = 10.0\n",
    "\n",
    "# Add the fake data to the ProcessedData object.\n",
    "fake_Sv.add_attribute('range', ranges)\n",
    "fake_Sv.add_attribute('ping_time', times.astype('datetime64[ms]'))\n",
    "fake_Sv.add_attribute('data', data)\n",
    "\n",
    "# Create lines.\n",
    "a_line = line.Line(ping_time=np.array([times[0], times[-1]]), data=np.array([\n",
    "    0, 0]))\n",
    "n_lines = int((ranges[-1] / (sample_thickness_m * 10)) + 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a matplotlib figure to plot our echograms on.\n",
    "fig_1 = figure()\n",
    "eg = echogram.Echogram(fig_1, fake_Sv, threshold=[0, 20])\n",
    "eg.axes.set_title(\"Echogram Plot Test\")\n",
    "\n",
    "for i in range(n_lines):\n",
    "    eg.plot_line(a_line, linewidth=1.0)\n",
    "    a_line = a_line + (sample_thickness_m * 10)\n",
    "\n",
    "# Display figure.\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
